For all the hype around artificial intelligence (AI), and the excitement around some of its potential – personal assistants that develop a personality, robot-assisted micro surgery, etc. – it is arguably adding most value to businesses in less glamorous, but ultimately more valuable, ways, says Nuxeo’s Dave Jones, in a guest blogpost.
Backend tasks in a business are few people’s favourite. They are hugely time consuming, rarely rewarding but are vitally important. Automating these tasks is an area where AI has the potential to add incredible value for businesses.
AI and information management
Information management is an area with many ways in which AI can be of benefit. AI allows organisations to streamline how they manage information, reduce storage, increase security, and deliver faster and more effective searches for content and information.
Many companies are struggling with the volume of information in modern business and find it difficult for users to locate important information that resides in multiple customer systems and transaction repositories. The key to solving this problem is having accurate metadata about each content and data asset. This makes it easy to quickly find information, and also provides context and intelligence to support key business processes and decisions.
Enrichment of metadata is one area that AI really excels at. Populating and changing metadata before AI was a laborious task – not made any easier by the fixed metadata schemas employed by many content management systems. However, metadata schemas in an AI-infused Content Services Platform (CSP) are flexible and extensible. Much more metadata is being stored and used than ever before, so the ability to use AI to process large volumes of content and create numerous and meaningful metadata tags is a potential game-changer.
Unlocking the content in legacy systems
Another powerful way in which AI can address backend tasks, is in connecting to content from multiple systems, whether on-premise or in the cloud. This ensures the content itself is left in place, but access is still provided to that content and data from the AI-infused CSP.
It also provides the ability for legacy content to make use of a modern metadata schema from the CSP – effectively enriching legacy content with metadata properties without making any changes to the legacy system at all. This is a compelling proposition in itself, but when combined with the automation of AI, even more so.
By using a CSP to pass content through an AI enrichment engine, that content can be potentially enriched with additional metadata attributes for each and every one of the files currently stored. This injects more context, intelligence, and insight into an information management ecosystem.
But by using an AI-driven engine to classify content stored within legacy systems, this becomes much easier to do. Even simple AI tools can identify the difference between a contract and a resume, but advanced engines expand this principle to build AI models based on content specific to an organisation. These will deliver much more detailed classifications than could ever be possible with generic classification.
Backend AI in action
A manufacturing firm I met with recently has been automating the classification and management of its CAD drawings. There is a misconception that AI needs to be super intelligent to add real value. But in this example the value of AI is not the intelligence required to identify what qualifies as a particular kind of design drawing, but to be ‘smart enough’ to recognise the documents that definitely ‘aren’t’ the right type – essentially to sift out the rubbish and allow people to focus on the relevant information much faster.
Information management and associated backend tasks may not be the most glamourous AI use cases but if done well, they can provide significant value to businesses all over the world.